Approximately 10% of all infants need some form of resuscitation at the time of birth and the interventions performed can have a significant impact on survival.1,2 Resuscitation is a complex process performed by a team of individuals coordinating many different functions. Substantial errors during resuscitations have been documented.3,4,5,6 Errors include lack of recognition of relevant details, and delayed or inconsistent performance of indicated interventions. These errors are frequently related to a lack of relevant situational awareness. This lack of situational awareness is often seen as a team focusing on only one aspect of the resuscitation at the expense of other aspects. We plan to devise a computerized system to be used during resuscitations which will generate prompts intended to focus the team's attention on the entire situation and function as reminders consistent with the resuscitation algorithm. For the most critically ill newborns, a delay or failue to perform a necessary intervention could be life threatening. Delays in achieving normal physiologic adaptation have the potential to contribute to impaired neurologic outcome in survivors. In the first minutes of life normal physiologic parameters, such as oxygen saturation and heart rate, are changing over time, making the identification of indicated interventions more complicated.7,8 The team must integrate the normal physiologic changes with the timeline of the resuscitation. The system that we will develop will display these data in real-time to the resuscitation team. The physiologic data will be used in conjunction with additional user input to generate appropriate voice prompts designed to focus the teams' attention on critical interventions in a timely manner. We intend to make the system upgradeable so that changes to the neonatal resuscitation algorithm can be reflected in the system as they occur. Institutions will be able to use the system to facilitate the performance of resuscitation and will have a simple method of performing their own continuous quality improvement using the data gathered. At the completion of this project we will have developed and tested in a simulation environment a decision support system which will provide appropriate audible and visual prompting during neonatal resuscitation. The system will later be tested in actual human neonatal resuscitations to determine if the use of this system reduces errors, helps maintain situational awareness of the entire team and leader, and most importantly improves resuscitation outcomes by encouraging more timely and relevant interventions. We believe that the use of such a system has the potential to significantly reduce neonatal morbidity and mortality in the most vulnerable and compromised infants.